Inside-outside: 3-D music through tissue conduction

Eliciting an auditory perception by means of mechanical transduction bypassing the peripheral hearing apparatus has been recorded as early as the 16th century. Excluding its audiometric use to assess ear pathology, bone and soft tissue conduction has received very little interest until the last two decades. Previous work during this time (Stanley and Walker 2006, MacDonald and Letowski 2006) has indicated robust lateralization is feasible via mechanical transduction. We have extended this, adding the front-back and up-down axes

Subjective differences between premium and mid-level digital hearing aids

This study compared perceptual differences between premium and mid-level hearing aids from a major manufacturer in normal hearing listeners. Limited literature currently exists comparing perceptual differences between premium and mid-level digital hearing aids. This information is highly important in decision-making for clinicians and patients alike. Barry et al. (2018) evaluated four major hearing aid models’ noise reduction properties and determined that one manufacturer’s premium and mid-level devices demonstrated significant differences in noise reduction gain in frequencies associated with human speech. We programmed this device for a mild sloping to moderately-severe SNHL using the manufacturer’s proprietary fitting formula and noise reduction at its maximum setting. The hearing aid was mounted on KEMAR and ten Hearing in Noise Test (HINT) sentences were recorded with each device (premium and mid-level) at two different signal to noise ratios (SNR): 0 dB SNR and +5 dB SNR. Normal hearing listeners (n = 19) were blindly presented with the pair of stimuli at each signal to noise ratio condition with a three-alternative forced choice paradigm, whereby they indicate which presentation they preferred, or if there was no perceptual differences between the recordings. The preferences were made by each subject on the basis of three different criteria: noisiness, speech intelligibility, and overall quality. The findings of this study are consistent with previous research and suggest that there is no subjective difference between premium and mid-level hearing aids on measures of noisiness, speech intelligibility, and overall sound quality. Overall, data suggested that participants did not perceive a statistically significant difference between technology levels for either the 0 dB SNR condition or the +5 dB SNR condition. This suggests that in both very noisy and less noisy environments, normal-hearing listeners do not perceive any advantage when listening with premium technology. Future research should examine premium and mid-level technology with objective outcome measures and utilize subjects with hearing loss. It may be useful to examine differences between the devices on measures of listening effort as well

Coding Strategies for Cochlear Implants Under Adverse Environments

Cochlear implants are electronic prosthetic devices that restores partial hearing in patients with severe to profound hearing loss. Although most coding strategies have significantly improved the perception of speech in quite listening conditions, there remains limitations on speech perception under adverse environments such as in background noise, reverberation and band-limited channels, and we propose strategies that improve the intelligibility of speech transmitted over the telephone networks, reverberated speech and speech in the presence of background noise. For telephone processed speech, we propose to examine the effects of adding low-frequency and high- frequency information to the band-limited telephone speech. Four listening conditions were designed to simulate the receiving frequency characteristics of telephone handsets. Results indicated improvement in cochlear implant and bimodal listening when telephone speech was augmented with high frequency information and therefore this study provides support for design of algorithms to extend the bandwidth towards higher frequencies. The results also indicated added benefit from hearing aids for bimodal listeners in all four types of listening conditions. Speech understanding in acoustically reverberant environments is always a difficult task for hearing impaired listeners. Reverberated sounds consists of direct sound, early reflections and late reflections. Late reflections are known to be detrimental to speech intelligibility. In this study, we propose a reverberation suppression strategy based on spectral subtraction to suppress the reverberant energies from late reflections. Results from listening tests for two reverberant conditions (RT60 = 0.3s and 1.0s) indicated significant improvement when stimuli was processed with SS strategy. The proposed strategy operates with little to no prior information on the signal and the room characteristics and therefore, can potentially be implemented in real-time CI speech processors. For speech in background noise, we propose a mechanism underlying the contribution of harmonics to the benefit of electroacoustic stimulations in cochlear implants. The proposed strategy is based on harmonic modeling and uses synthesis driven approach to synthesize the harmonics in voiced segments of speech. Based on objective measures, results indicated improvement in speech quality. This study warrants further work into development of algorithms to regenerate harmonics of voiced segments in the presence of noise

Objective Assessment of Machine Learning Algorithms for Speech Enhancement in Hearing Aids

Speech enhancement in assistive hearing devices has been an area of research for many decades. Noise reduction is particularly challenging because of the wide variety of noise sources and the non-stationarity of speech and noise. Digital signal processing (DSP) algorithms deployed in modern hearing aids for noise reduction rely on certain assumptions on the statistical properties of undesired signals. This could be disadvantageous in accurate estimation of different noise types, which subsequently leads to suboptimal noise reduction. In this research, a relatively unexplored technique based on deep learning, i.e. Recurrent Neural Network (RNN), is used to perform noise reduction and dereverberation for assisting hearing-impaired listeners. For noise reduction, the performance of the deep learning model was evaluated objectively and compared with that of open Master Hearing Aid (openMHA), a conventional signal processing based framework, and a Deep Neural Network (DNN) based model. It was found that the RNN model can suppress noise and improve speech understanding better than the conventional hearing aid noise reduction algorithm and the DNN model. The same RNN model was shown to reduce reverberation components with proper training. A real-time implementation of the deep learning model is also discussed

Electroacoustic and Behavioural Evaluation of Hearing Aid Digital Signal Processing Features

Modern digital hearing aids provide an array of features to improve the user listening experience. As the features become more advanced and interdependent, it becomes increasingly necessary to develop accurate and cost-effective methods to evaluate their performance. Subjective experiments are an accurate method to determine hearing aid performance but they come with a high monetary and time cost. Four studies that develop and evaluate electroacoustic hearing aid feature evaluation techniques are presented. The first study applies a recent speech quality metric to two bilateral wireless hearing aids with various features enabled in a variety of environmental conditions. The study shows that accurate speech quality predictions are made with a reduced version of the original metric, and that a portion of the original metric does not perform well when applied to a novel subjective speech quality rating database. The second study presents a reference free (non-intrusive) electroacoustic speech quality metric developed specifically for hearing aid applications and compares its performance to a recent intrusive metric. The non-intrusive metric offers the advantage of eliminating the need for a shaped reference signal and can be used in real time applications but requires a sacrifice in prediction accuracy. The third study investigates the digital noise reduction performance of seven recent hearing aid models. An electroacoustic measurement system is presented that allows the noise and speech signals to be separated from hearing aid recordings. It is shown how this can be used to investigate digital noise reduction performance through the application of speech quality and speech intelligibility measures. It is also shown how the system can be used to quantify digital noise reduction attack times. The fourth study presents a turntable-based system to investigate hearing aid directionality performance. Two methods to extract the signal of interest are described. Polar plots are presented for a number of hearing aid models from recordings generated in both the free-field and from a head-and-torso simulator. It is expected that the proposed electroacoustic techniques will assist Audiologists and hearing researchers in choosing, benchmarking, and fine-tuning hearing aid features

The effects of hearing aid circuitry and speech presentation level on acceptance of background noise

The present study investigated the effects of hearing aid circuitry and speech presentation level on ANL and hearing in noise in 19 adult, bilateral hearing aid users. The acceptable noise level (ANL) procedure was used to assess acceptance of background noise. Conventional ANLs (i.e., measured at the participant\u27s most comfortable listening level (MCL)) and ANLs at eight fixed speech presentation levels were obtained. Then global ANLs (i.e., ANLs averaged over eight fixed speech presentation levels) and ANL growth (i.e., the slope of the ANL function) were calculated Each measure was obtained in three conditions: unaided, aided with wide dynamic range (WDRC) circuitry, and aided with output limiting compression (dSC) circuitry. Results revealed that conventional ANLs are not significantly different when obtained using any of the three levels of hearing aid circuitry. However, results demonstrated that global ANLs may be affected by hearing aid circuitry in that listeners are able to accept more background noise when in the unaided or dSC circuitry condition compared to using WDRC. Finally, results showed that ANL growth for each type of hearing aid circuit was not significantly different, indicating that ANL growth is stable for all three types of circuitry